Determining a seismic quality factor for subsurface formations for marine vertical seismic profiles

ABSTRACT

A seismic attenuation quality factor Q is determined for seismic signals at intervals of subsurface formations between a seismic source at a marine level surface and one or more receivers of a well. Hydrophone and geophone data are obtained. A reference trace is generated from the hydrophone and geophone data. Vertical seismic profile (VSP) traces are received. First break picking of the VSP traces is performed. VSP data representing particle motion measured by a receiver of the well are generated. The reference trace is injected into the VSP data. A ratio of spectral amplitudes of a direct arrival event of the VSP data and the reference trace is determined. From the ratio, a quality factor Q is generated representing a time and depth compensated attenuation value of seismic signals between the seismic source at the marine level surface and the first receiver.

TECHNICAL FIELD

The present disclosure generally relates to an approach for identifyinggeologic features in subsurface formations including deriving seismicabsorption quality factors (Q) from vertical seismic profiles (VSP's) inmarine contexts.

BACKGROUND

In geology, sedimentary facies are bodies of sediment that arerecognizably distinct from adjacent sediments that resulted fromdifferent depositional environments. Generally, geologists distinguishfacies by aspects of the rock or sediment being studied. Seismic faciesare groups of seismic reflections whose parameters (such as amplitude,continuity, reflection geometry, and frequency) differ from those ofadjacent groups. Seismic facies analysis, a subdivision of seismicstratigraphy, plays an important role in hydrocarbon exploration and isone key step in the interpretation of seismic data for reservoircharacterization. The seismic facies in a given geological area canprovide useful information, particularly about the types of sedimentarydeposits and the anticipated lithology.

In reflection seismology, geologists and geophysicists perform seismicsurveys to map and interpret sedimentary facies and other geologicfeatures for applications including identification of potentialpetroleum reservoirs. Seismic surveys are conducted by using acontrolled seismic source (for example, a seismic vibrator or dynamite)to create seismic waves. The seismic source is typically located atground surface. Seismic body waves travel into the ground, are reflectedby subsurface formations, and return to the surface where they recordedby sensors called geophones. Seismic surface waves travel along theground surface and diminish as they get further from the surface.Seismic surface waves travel more slowly than seismic body waves. Thegeologists and geophysicists analyze the time it takes for the seismicbody waves to reflect off subsurface formations and return to thesurface to map sedimentary facies and other geologic features.Similarly, analysis of the time it takes seismic surface waves to travelfrom source to sensor can provide information about near surfacefeatures. This analysis can also incorporate data from sources, forexample, borehole logging, gravity surveys, and magnetic surveys.

One approach to this analysis is based on tracing and correlating alongcontinuous reflectors throughout the dataset produced by the seismicsurvey to produce structural maps that reflect the spatial variation indepth of certain facies. These maps can be used to identify impermeablelayers and faults that can trap hydrocarbons such as oil and gas.

SUMMARY

This disclosure describes methods and systems to estimate an elasticattenuation quality factor model (also called a Q factor, Q value, Qmodel, or simply Q) from Vertical Seismic Profile (VSP) data. In seismicmodeling and analysis, the quality factor Q quantifies the energy lossof a propagating wavelet with time due to fluid movement and frictionwith grain boundary. This can also be referred to as a seismicattenuation factor. The effect can be referred to as Earth's absorption,and can be considered to be an undesired distortion to the signalwavelet.

Generally, Q models are used to remove the effects of Earth's absorptionfrom surface seismic reflection data. This restores high-frequencysignal amplitudes, leading to higher resolution images and betteramplitude analysis of seismic data. A more accurate model improves thiscorrection. This inverse-Q filtering uses Q values for the completeseismic ray path from the source location to receiver. A Q-model forentire seismic trace records is thus used to perform the correction. Theprocess described in this specification provides Q-values that areotherwise missing or inaccurate for existing VSP's. The improved Qestimates from the methods subsequently described enable more detailedand more accurate Q models for inverse-Q filtering of surface seismicdata.

The data processing system and processes described in this specificationprovide one or more advantages. The methods and systems subsequentlydescribed overcome an issue of the poor quality of a first number oftraces (such as the first few traces) due to an existence of multiplecasing strings in a seismic borehole. In marine contexts, a referencetrace that is typically used for Q estimation is deeper than apreferable, shallower reference traces that provides data for ashallowest part of a local geology (subsurface). The result is arelatively poor Q estimation, because the reference trace represents ageology that is deeper than where most subsurface attenuation occurs.

To overcome this issue, the methods and systems described in thisspecification include deploying a dual sensor at a sea floor. The sensorincludes a hydrophone component and a vertical geophone component. Formarine VSP, a near-field hydrophone is generally deployed to account fortrigger delay. The system uses a matching filter between a hydrophoneresponse and a geophone response to eliminate a source ghost thattypically interferes with direct wave arrivals from the sensor.

The hydrophone component is a scalar. At the sea floor, the source ghosthas an opposite polarity to the geophone. A summation of these signalsremoves the source ghosting from the seismic signals. A resulting traceis used as the reference trace for Q estimation. The systems and methodsdescribed in this specification, by resolving interference form sourceghosting, provide a better source signature deconvolution operator for Qmodeling. The systems and methods ensure that the delivered Q values donot have the Q effect of the seismic signal traveling through the water.

In a general aspect, a method is for determining a seismic attenuationquality factor Q for seismic signals at intervals of subsurfaceformations between a seismic source at a marine level surface and one ormore receivers of a well. The method includes obtaining sensor data froma dual sensor comprising a hydrophone and a geophone, the sensor dataincluding a hydrophone component and a geophone component; generating areference trace from the hydrophone component and the geophone componentof the sensor data; receiving vertical seismic profile traces; perform afirst break picking of the vertical seismic profile traces; generating,based on the first break picking, vertical seismic profile datarepresenting particle motion measured by a first receiver of the one ormore receivers of the well; injecting the reference trace into thevertical seismic profile data; determining a ratio of spectralamplitudes of a direct arrival event of the vertical seismic profiledata and the reference trace; and generating, from the ratio, a qualityfactor Q representing a time and depth compensated attenuation value ofseismic signals between the seismic source at the marine level surfaceand the first receiver.

In some implementations, generating a reference trace from thehydrophone component and the geophone component of the sensor datacomprises: applying a matching filter to transform the hydrophonecomponent of the sensor data to match the geophone component; andsumming the transformed hydrophone component and the geophone component.

In some implementations, the method includes comparing each trace of thevertical seismic profile traces to a quality threshold; and removing,from the vertical seismic profile traces, each trace that does notsatisfy the quality threshold.

In some implementations, the method includes determining a geometry ofthe well and a location of the dual sensor; and performing a correctionon the vertical seismic profile traces to account for a geometricalspreading of propagating seismic signals used to generate the verticalseismic profile data.

In some implementations, the dual sensor is deployed at or near a welllocation at the marine level surface of the subsurface formation.

In some implementations, the ratio is defined by

${Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}},{m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}},$where A₁ and A₂ are the spectral amplitudes for direct arrivals attravel times, t₁ and t₂ recorded by receivers at depths d₁ and d₂, f isfrequency, ln is a natural logarithm, and m is the natural logarithm ofthe ratio of the spectral amplitudes. In some implementations, A₁ is aKlauder wavelet amplitude spectra at time t₁=0 seconds and depth d₁=0meters, and A₂ is the amplitude spectra of the direct arrival event attime t₂ of the first receiver from the marine level surface at depthd₂=0 meters.

In a general aspect, a system determines a seismic attenuation qualityfactor Q for seismic signals at intervals of subsurface formationsbetween a seismic source at a marine level surface and one or morereceivers of a well. The system includes at least one processor; and amemory storing instructions that, when executed by the at least oneprocessor, cause the at least one processor to perform operationscomprising: obtaining sensor data from a dual sensor comprising ahydrophone and a geophone, the sensor data including a hydrophonecomponent and a geophone component; generating a reference trace fromthe hydrophone component and the geophone component of the sensor data;receiving vertical seismic profile traces; perform a first break pickingof the vertical seismic profile traces; generating, based on the firstbreak picking, vertical seismic profile data representing particlemotion measured by a first receiver of the one or more receivers of thewell; inject the reference trace into the vertical seismic profile data;determining a ratio of spectral amplitudes of a direct arrival event ofthe vertical seismic profile data and the reference trace; andgenerating, from the ratio, a quality factor Q representing a time anddepth compensated attenuation value of seismic signals between theseismic source at the marine level surface and the first receiver.

In some implementations, generating a reference trace from thehydrophone component and the geophone component of the sensor datacomprises: applying a matching filter to transform the hydrophonecomponent of the sensor data to match the geophone component; andsumming the transformed hydrophone component and the geophone component.

In some implementations, the operations further comprise comparing eachtrace of the vertical seismic profile traces to a quality threshold; andremoving, from the vertical seismic profile traces, each trace that doesnot satisfy the quality threshold.

In some implementations, the operations further comprise determining ageometry of the well and a location of the dual sensor; and performing acorrection on the vertical seismic profile traces to account for ageometrical spreading of propagating seismic signals used to generatethe vertical seismic profile data.

In some implementations, the dual sensor is deployed at or near a welllocation at the marine level surface of the subsurface formation.

In a general aspect, one or more non-transitory computer readable mediastore instructions for determining a seismic attenuation quality factorQ for seismic signals at intervals of subsurface formations between aseismic source at a marine level surface and one or more receivers of awell. The one or more instructions are configured to cause at least oneprocessor, when executed by the at least one processor, to performoperations comprising: obtaining sensor data from a dual sensorcomprising a hydrophone and a geophone, the sensor data including ahydrophone component and a geophone component; generating a referencetrace from the hydrophone component and the geophone component of thesensor data; receiving vertical seismic profile traces; perform a firstbreak picking of the vertical seismic profile traces; generating, basedon the first break picking, vertical seismic profile data representingparticle motion measured by a first receiver of the one or morereceivers of the well; inject the reference trace into the verticalseismic profile data; determining a ratio of spectral amplitudes of adirect arrival event of the vertical seismic profile data and thereference trace; and generating, from the ratio, a quality factor Qrepresenting a time and depth compensated attenuation value of seismicsignals between the seismic source at the marine level surface and thefirst receiver.

In some implementations, generating a reference trace from thehydrophone component and the geophone component of the sensor datacomprises: applying a matching filter to transform the hydrophonecomponent of the sensor data to match the geophone component; andsumming the transformed hydrophone component and the geophone component.

In some implementations, the operations further comprise comparing eachtrace of the vertical seismic profile traces to a quality threshold; andremoving, from the vertical seismic profile traces, each trace that doesnot satisfy the quality threshold.

In some implementations, the operations further comprise determining ageometry of the well and a location of the dual sensor; and performing acorrection on the vertical seismic profile traces to account for ageometrical spreading of propagating seismic signals used to generatethe vertical seismic profile data.

In some implementations, the dual sensor is deployed at or near a welllocation at the marine level surface of the subsurface formation.

In some implementations, the ratio is defined by

${Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}},{m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}},$where A₁ and A₂ are the spectral amplitudes for direct arrivals attravel times, t₁ and t₂ recorded by receivers at depths d₁ and d₂, f isfrequency, ln is a natural logarithm, and m is the natural logarithm ofthe ratio of the spectral amplitudes. In some implementations, A₁ is aKlauder wavelet amplitude spectra at time t₁=0 seconds and depth d₁=0meters, and A₂ is the amplitude spectra of the direct arrival event attime t₂ of the first receiver from the marine level surface at depthd₂=0 meters.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of a seismic survey being performed to mapsubterranean features such as facies and faults.

FIG. 2 illustrates a three-dimensional cube representing a subterraneanformation.

FIG. 3 illustrates a stratigraphic trace within the three-dimensionalcube of FIG. 2 .

FIGS. 4A, 4B, and 4C schematically illustrate the process stacking agroup of seismic traces to improve the signal to noise ratio of thetraces.

FIG. 5 shows a process for estimating seismic attenuation factor valuesfor subsurface regions below a sea floor.

FIG. 6 shows a data processing system for estimating seismic attenuationfactor values for subsurface regions below a sea floor.

FIG. 7 shows an example well.

FIG. 8A shows an example of a hydrophone response and a geophoneresponse.

FIG. 8B shows an example of a filtered hydrophone response and afiltered geophone response after match filtering is performed on thehydrophone response and the geophone response of FIG. 8A.

FIG. 9 is a diagram of an example computing system.

DETAILED DESCRIPTION

This specification describes workflows for, but is not limited to,estimating seismic attenuation factor values for subsurface regionsbelow a sea floor. It is presented to enable any person skilled in theart to make and use the disclosed subject matter in the context of oneor more particular implementations. Various modifications, alterations,and permutations of the disclosed implementations can be made and willbe readily apparent to those skilled in the art, and the generalprinciples defined may be applied to other implementations andapplications without departing from the scope of the disclosure. Thus,the present disclosure is not intended to be limited to the described orillustrated implementations, but is to be accorded the widest scopeconsistent with the principles and features disclosed.

This specification describes data processing systems and methods forgenerating quality factor (Q) models for the subsurface from the seafloor and below. Generally, Q represents a ratio of stored energy todispersed energy, and measures a relative energy loss per oscillationcycle. The data processing system is configured to remove the effects ofEarth's absorption from surface seismic reflection data. Generally, whenQ models are more accurate, removal of these absorption effects isimproved. This can be called inverse-Q filtering. Inverse-Q filtering isdetermined using Q values for the complete seismic ray path from thesource location to receiver. The data processing system can use aQ-model for entire seismic trace records, from the marine surface to adeepest reflector. This includes shallow portion of the model for theinterval from the VSP source to the first useable VSP downhole receiver.

For Q derivation, the data processing system is configured to provide Qestimation values for the shallow region, or the region between themarine surface and the first downhole receiver of the seismic sensorarray. This workflow is performed by a computing system (such ascomputing system 124 described in relation to FIG. 1 ), including one ormore processing devices in communication with one or more sensors, toderive Q-values for the interval from source located at the marinesurface to a first useable VSP receiver, which would generally otherwisebe estimated by extrapolation from deeper Q-values. In other words, theworkflow generates the missing shallow Q-values, enabling more detailedand more accurate Q models required for inverse-Q filtering of marinesurface seismic data.

In contrast to land seismic acquisition, workflows for marine seismicacquisition use an impulsive source. Cross correlation with the pilot isgenerally unavailable for obtaining the best downgoing waves for Qestimation without interference caused by signal multiples and ghosteffects. When using an impulsive source for marine vertical seismicimaging, the system uses a source signature deconvolution operator toeliminate ghost effects from the data. To obtain a reference trace,deploying dual sensor will provide us with the reference trace for Qestimation tackling the main two challenges, which are the sourcesignature using far field hydrophone and the shallowest reference tracestarting from sea floor for Q computation.

Generally, downhole VSP receivers are either geophones (such as particlevelocity meters) or accelerometers (particle acceleration meters). Inthe case of the later, the acceleration traces are typically transformedto velocity by removal of the accelerometer impulse response (theresponse to an acceleration impulse) and time-integration to velocity.If geophone sensors are used, an inverse filter is applied to remove thegeophone impulse response (the response of a geophone to a velocityimpulse). A Vibroseis source has a far-field particle displacement thatis proportional to the sweep ground force. In the present disclosure,the data processing system transfers the particle motion of the receiverto that of the Vibroseis source by time-integration to displacement.

FIG. 1 is a schematic view of a seismic survey being performed to mapsubterranean features such as facies and faults in a subterraneanformation 100 under a marine feature (such as under the sea or ocean).The seismic survey can be conducted for seismic imaging of asubterranean geological formation under the marine surface using theimproved estimation of Q near the marine surface, as described in thisspecification. The subterranean formation 100 includes a layer ofimpermeable cap rock 102 at the surface. Facies underlying theimpermeable cap rocks 102 include a sandstone layer 104, a limestonelayer 106, and a sand layer 108. A fault line 110 extends across thesandstone layer 104 and the limestone layer 106.

Oil and gas tend to rise through permeable reservoir rock until furtherupward migration is blocked, for example, by the layer of impermeablecap rock 102. Seismic surveys attempt to identify locations whereinteraction between layers of the subterranean formation 100 are likelyto trap oil and gas by limiting this upward migration. For example, FIG.1 shows an anticline trap 107, where the layer of impermeable cap rock102 has an upward convex configuration, and a fault trap 109, where thefault line 110 might allow oil and gas to flow in with clay materialbetween the walls traps the petroleum. Other traps include salt domesand stratigraphic traps.

A seismic source 112 (for example, a seismic vibrator or an explosion)generates seismic waves that propagate in the earth. Althoughillustrated as a single component in FIG. 1 , the source or sources 112are typically a line or an array of sources 112. The generated seismicwaves include seismic body waves 114 that travel into the ground andseismic surface waves 115 travel along the ground surface and diminishas they get further from the marine surface. The computing system isconfigured to estimate the seismic attenuation factor Q near the marinesurface by using a dual-sensor approach. This provides a reference tracefor Q estimation by using the source signature of a far field hydrophonesensor and the shallowest reference trace relative to the marinesurface. The dual sensor configuration and near surface regions aredescribed in relation to FIG. 7 .

The velocity of these seismic waves depends on properties, for example,density, porosity, and fluid content of the medium through which theseismic waves are traveling. Different geologic bodies or layers in theearth are distinguishable because the layers have different propertiesand, thus, different characteristic seismic velocities. For example, inthe subterranean formation 100, the velocity of seismic waves travelingthrough the subterranean formation 100 will be different in thesandstone layer 104, the limestone layer 106, and the sand layer 108. Asthe seismic body waves 114 contact interfaces between geologic bodies orlayers that have different velocities, each interface reflects some ofthe energy of the seismic wave and refracts some of the energy of theseismic wave. Such interfaces are sometimes referred to as horizons.

The seismic body waves 114 are received by a sensor or sensors 116.Although illustrated as a single component in FIG. 1 , the sensor orsensors 116 are typically a line or an array of sensors 116 thatgenerate an output signal in response to received seismic wavesincluding waves reflected by the horizons in the subterranean formation100. The sensors 116 can be geophone-receivers that produce electricaloutput signals transmitted as input data, for example, to a computer 118on a seismic control truck 120. Based on the input data, the computer118 may generate a seismic data output, for example, a seismic two-wayresponse time plot. Another example of the sensors 116 is described inrelation to in FIG. 7 .

The seismic surface waves 115 travel more slowly than seismic body waves114. Analysis of the time it takes seismic surface waves 115 to travelfrom source to sensor can provide information about near surfacefeatures.

A control center 122 can be operatively coupled to the seismic controltruck 120 and other data acquisition and wellsite systems. The controlcenter 122 may have computer facilities for receiving, storing,processing, and analyzing data from the seismic control truck 120 andother data acquisition and wellsite systems. For example, computersystems 124 in the control center 122 can be configured to analyze,model, control, optimize, or perform management tasks of fieldoperations associated with development and production of resources suchas oil and gas from the subterranean formation 100. Alternatively, thecomputer systems 124 can be located in a different location than thecontrol center 122. Some computer systems are provided withfunctionality for manipulating and analyzing the data, such asperforming seismic interpretation or borehole resistivity image loginterpretation to identify geological surfaces in the subterraneanformation or performing simulation, planning, and optimization ofproduction operations of the wellsite systems.

In some embodiments, results generated by the computer systems 124 maybe displayed for user viewing using local or remote monitors or otherdisplay units. One approach to analyzing seismic data is to associatethe data with portions of a seismic cube representing the subterraneanformation 100. The seismic cube can also display results of the analysisof the seismic data associated with the seismic survey.

FIG. 2 illustrates a seismic cube 140 representing at least a portion ofthe subterranean formation 100. The seismic cube 140 is composed of anumber of voxels 150. A voxel is a volume element, and each voxelcorresponds, for example, with a seismic sample along a seismic trace.The cubic volume C is composed along intersection axes of offset spacingtimes based on a delta-X offset spacing 152, a delta-Y offset spacing154, and an offset spacing 156. Within each voxel 150, statisticalanalysis can be performed on data assigned to that voxel to determine,for example, multimodal distributions of travel times and derive robusttravel time estimates (according to mean, median, mode, standarddeviation, kurtosis, and other suitable statistical accuracy analyticalmeasures) related to azimuthal sectors allocated to the voxel 150.

FIG. 3 illustrates a seismic cube 200 representing a formation. Theseismic cube has a stratum 202 based on a surface (for example, anamplitude surface 204) and a stratigraphic horizon 206. The amplitudesurface 204 and the stratigraphic horizon 206 are grids that includemany cells such as exemplary cell 208. Each cell is a sample of aseismic trace representing an acoustic wave. Each seismic trace has anx-coordinate and a y-coordinate, and each data point of the tracecorresponds to a certain seismic travel time or depth (t or z). For thestratigraphic horizon 206, a time value is determined and then assignedto the cells from the stratum 202. For the amplitude surface 204, theamplitude value of the seismic trace at the time of the correspondinghorizon is assigned to the cell. This assignment process is repeated forall of the cells on this horizon to generate the amplitude surface 204for the stratum 202. In some instances, the amplitude values of theseismic trace 210 within window 212 by horizon 206 are combined togenerate a compound amplitude value for stratum 202. In these instances,the compound amplitude value can be the arithmetic mean of the positiveamplitudes within the duration of the window, multiplied by the numberof seismic samples in the window.

FIGS. 4A, 4B, and 4C schematically illustrate the process stacking agroup of seismic traces 210 to improve the signal to noise ratio of thetraces. FIG. 4A illustrates a common midpoint (CMP) gather of eighttraces 210 generated by a set of sources and sensors that share a commonmidpoint. For ease of explanation, the traces are assumed to have beengenerated by reflections from three horizontal horizons.

The traces 210 are arranged with increasing offset from the CMP. Theoffset of the traces 210 from the CMP increase from left to right andthe reflection time increases from top to bottom. Increasing offset fromthe common midpoint increases the angle of a seismic wave that between asource and a sensor, increases the distance the wave travels between thesource and the sensor, and increases the slant reflection time. Theincreasing time for the reflections (R₁, R₂, and R₃) from each of thehorizons to arrive for source-sensor pairs with increasing offsets fromthe CMP reflects this increased slant time.

FIG. 4B shows the traces 210 after normal moveout (NMO) correction. NMOis the difference between vertical reflection time and the slantreflection time for a given source-sensor pair. This correction placesreflections (R₁, R₂, and R₃) from common horizons at the same arrivaltime. The NMO correction is a function of the vertical reflection timefor a specific horizon, the offset of a specific source-sensor pair, andthe velocity of the seismic wave in the subterranean formation. Thevertical reflection time for a specific horizon and the offset for aspecific source-sensor pair are known parameters for each trace.However, the velocity is usually not readily available. As previouslydiscussed, the velocity of seismic waves depends on properties, forexample, density, porosity, and fluid content of the medium throughwhich the seismic waves are traveling and consequently varies withlocation in the subterranean formation being studied.

FIG. 4C shows a stack trace 214 generated by summing the traces 210 ofthe CMP gather and dividing the resulting amplitudes by the number oftraces in the gather. The number of traces in the gather is alsoreferred to as the fold of the gather. The noise tends to cancel out andthe reflections (R₁, R₂, and R₃) from the horizons of the subterraneanformation are enhanced.

Turning to FIGS. 5-6 , a data processing system 250 and process 220described in relation to FIGS. 5-6 are configured to estimate the Qfactor for VSPs near a marine surface. FIG. 5 shows a process 220 fordetermining a seismic attenuation factor for near-surface subsurfaceformations. The recorded signal at the receiver and a numericalrepresentation of the generated signal at the source can be used by thedata processing system 250 of FIG. 6 for performing the process 220 ofFIG. 5 . In an aspect, a quality factor engine 254 of the dataprocessing system 250 is configured to receive the seismic data 252 andgenerate Q factor values data 262 of the intervals using a series ofmodules. The quality factor engine 254 can include a vertical seismicprofile module 256, and a quality factor estimation module 258.

To perform the process 220 of FIG. 5 , the data processing system 250receives (222) or obtains data from the dual sensor at the marinesurface. The dual sensor includes a hydrophone and a geophone. The dualsensor is configured to provide at least the vertical component for eachresponse of the hydrophone and the geophone. While all three components(including an x-component, a y-component, and a z-component) can beincluded, only the vertical component is used for the process 220. Thehydrophone response is different from the geophone response. Thehydrophone response includes a scalar measurement that is not affectedby a direction from which the seismic signal (the seismic vibrations orwaves) are received in the hydrophone. The geophone response includes avector. A polarity of the response changes depending on a direction fromwhich the seismic signal arrives at the geophone. The data processingsystem 250 is configured to transform the hydrophone response into ageophone response signal. The data processing system 250 performs thetransformation by applying a matching filter to the hydrophone signaland the geophone signal.

FIG. 8A shows a graph 300 including an example of a hydrophone response304 a and a geophone response 306 a measured at the dual sensor. Thehydrophone response 304 a and the geophone response 306 a each aremeasurements at the hydrophone and geophone, respectively, of theseismic signal in the subterranean formation. The hydrophone response304 a and the geophone response 306 a have not been filtered orprocessed as shown in graph 300. As shown in the geophone response 306a, a ghost signal 308 is shown following the first breaks 302 a of thesignal. To remove the ghost signal, match filtering is performed, assubsequently discussed.

The dual sensor including the geophone and hydrophone is deployed closeto the well location. The proximity of the dual sensor to the wellenables the data processing system 250 to use the signals provided bythe dual sensor for performing a cross ghosting signature summation. Toestimate Q properly, the reference trace should be free of source ghostsignals that interfere with the direct arrival energy.

The hydrophone component of the signal is a scalar quantity. The ghostsignal has an opposite polarity for the geophone component of the dualsensor than for the hydrophone component. The matching filter is appliedby the data processing system 250 for each of amplitude and phase of thedual sensor signals. Application of the matching enables the dataprocessing system 250 to sum the hydrophone and geophone signals (224)to remove the source ghost effect. The resulting signal represents alocation at the marine surface (such as a sea floor) is used as thereference trace for Q estimation in the VSP data. FIG. 8B shows anexample of a filtered hydrophone response 304 b and a filtered geophoneresponse 306 b after match filtering is performed on the geophoneresponse 304 a of FIG. 8A. The matching filter is applied to each of theamplitude and phases of the geophone response 306 a. As shown in graph310, the hydrophone response 304 b is unchanged from the hydrophoneresponse 304 a. The ghost effects 308 of the geophone response 306 a areremoved from the filtered geophone response 306 b. Instead, the firstbreaks 302 b of the filtered response 306 b show no ghost signals.

The data processing system 250 determines a source signaturedeconvolution operator (226). The data processing system 250 records thesource signature using a far-field hydrophone sensor before the signaltravels through the subterranean environment, which has an impulseresponse. The recorded seismic data is represented by a convolutionalmodel x(t)=s(t)*w(t)*e(t), where s(t) is the source signature, w(t) isthe earth's effective wavelet, and e(t) is reflectivity. The dataprocessing system 250 deconvolves the signal from the data to obtain asignal for increased temporal and vertical resolution, relative to asignal for which far-field hydrophone data (and subsequentdeconvolution) are not performed.

The data processing system 250 performs obtains data including the rawpre-stack gathers for the VSP vertical component and the other twohorizontal components. The data processing system 250 determines a wellgeometry (228). The data processing system 250 determines the geometryof the dual sensor with respect to the well. The data processing systemdetermines values for deviation, and determines values for the shotlocation and offset values. Not all wells are vertical, and all welldata are recorded in measured depth and not true vertical depth. For Qestimation, the data processing system 250 estimates attenuation as ananelastic rock property using a true vertical depth. The data processingsystem 250 receives geometry data for the well deviation and shotsoffsets and applies the true depths for Q estimation.

The data processing system 250 performs a first pass of a Quality Check(QC). This includes an analysis by the data processing system 250 in thepre-stack domain of each trace in the data. The data processing system250 deletes necessary bad traces from all three components. Many factorsdefine bad traces. A primary factor is a quality of first break picks ofthe signal (seen in FIGS. 8A-8B). If a trace does not show clear firstbreaks, the trace is eliminated from the Q estimation. Other factorsthat affect the trace quality include a broadening of the wavelet, ghostcontamination, and signal multiples. The data processing system 250estimates Q on downgoing waves. When the data processing system 250cannot isolate the downgoing waves for certain traces within one waveletlength, the data processing system eliminates the trace. In thiscontext, one wavelet length is the distance between two troughs or twopeaks in the wavelet.

The data processing system 250 performs a stacking of the edited gathers(230) and performs first break picking (232) on the vertical componentto create a velocity profile for the subsurface. The data processingsystem 250 sorts the VSP in a time axis and depth axis (T-X domain). Theexact depth of the receiver is known, and therefore how long the signaltook to travel from the source to that receiver. The data processingsystem 250 picks first breaks to provide a time-depth relationship andaccurate interval velocity measurements. The data processing system 250uses the first breaks data to estimate geometrical spreading correction(spherical divergence correction). The data processing system 250 alsouses the first breaks data to acquire downgoing waves, during wavefieldseparation, for estimation of Q. Many approaches to wavefield separationof geometrical spreading correction are possible, and Q estimation canbe performed for any data that is corrected for geometrical spreadingusing any type of wavefield separation method.

The data processing system 250 performs a correction for geometricalspreading (234) related to the physics of how waves propagate. Toperform this step, the data processing system 250 performs acompensation of spherical divergence using the velocity profilecalculated from the first break picks.

The data processing system 250 performs an estimation and compensationof the anelastic attenuation Q value. In typical VSPs, the first fewtraces are of relatively low quality due to the existence of multiplecasing strings in the borehole. Here, a low quality trace refers to atrace from which Q values cannot be estimated due to a low quality offirst breaks (as previously described). If first breaks cannot bepicked, the trace is removed. Another factor includes a signal to noiseratio. If a trace has a lower signal to noise ratio than the rest of thedata (by a given threshold amount), the data processing system 250removes the trace from the analysis because that trace is highlycontaminated by noise. As previously described, another factor is thewavefield separation of a trace. When the data processing system 250cannot isolate the downgoing signal properly (due to a ghost effect,multiple contamination, upgoing leakage, and so forth), the trace isremoved.

The data processing system 250 injects or includes the stackedhydrophone component (236) as the reference trace in time and depthvariant Q estimation. To inject the hydrophone component, the dataprocessing system 250 combines the hydrophone with the VSP record as afirst trace and assigns a depth of 0 because the reference trace islocated at the sea floor.

The data processing system 250 estimates both time and depth-variant Qvalues (238) using a spectral ratio method. The data processing system250 sums the transformed hydrophone component with the geophonecomponent of the dual sensor to be the shallowest reference trace. Thereference trace is typically of low quality because the verticalcomponent of the VSP (ZVSP) does not have good data starting from themarine surface. The process 220 increases seismic resolution bycompensating for the loss of the higher frequencies related to Q.Resolution increase depends on recovered frequencies. In an example, theenergy is equivalent to 120 Hz. Depending on absorption rate and anelasticity of the subterranean formation, there can be 60 Hz in thedata, 80 Hz, and so forth. Seismic resolution is a factor of thevelocity of the subsurface and the frequency content of the data.Additionally, the magnitude of improvement varies depending on a geologyand a depth of the well. Because of these factors, resolution increasecan vary between 10% to 100% better resolution.

The cross ghosting provides better transformation of the hydrophoneresponse into the geophone response to ensure that the estimated Q valuerepresents geology and not a numerical error from the matching filter.The dual sensor as a reference trace provides an effective Q for thenear field hydrophone component, rather than the signal travellingthrough water to reach the VSP sensors down the well. The dataprocessing system 250 uses the hydrophone response summed with geophonefrom the dual sensor deployed at the sea floor as the reference trace,and assigns the trace a depth of zero. Time and depth variant Q valuesare compensated to recover amplitude and phase changes due to the wavespropagating in the subsurface geology.

The process described in this specification provides a more accurateestimation of Q than those performed without the reference traceprovided from the dual sensor that is added. The near-surface data inVSP surveys (such as near the marine surface) are of relatively lowquality in relation to typical traces of the stack. The near surfacetraces provide an inaccurate estimate of Q values. By injecting thehydrophone component data from the dual sensor as the reference trace,as previously discussed, the inverse Q filter applied for signalprocessing the seismic data provides more realistic amplitudes and phasecorrection for the seismic data. This approach can also on surfaceseismic data, and is not limited to VSP only.

The system 250 and method 220 thus provide the following features forseismic imaging. The system 250 and method 220 provide a derivation ofthe attenuation factor Q using the dual sensor signal as a shallowestreference trace. The system 250 and method 220 eliminate the sourceghost effect from the data. The system 250 and method 220 enable aproper source signature deconvolution for marine VSP data. The system250 and method 220 reduce uncertainties of depth for lookahead VSPprocesses.

As previously discussed, the data processing system 250 is configured toapply a spectral ratio method previously described, such as by thequality factor estimation module 258. The data processing system 250determines (238) the spectral ratio of the spectra of the referencetrace and the seismic trace. For example, the data processing system 250obtains the spectral ratio of the amplitudes of the Klauder wavelet andthe direct arrival event of the transformed receiver seismic trace ofthe first useable downhole receiver. The data processing system 250generates a value for the quality factor Q, such as by quality factorestimation module 258. The Q factor can represent an attenuation of theseismic trace. The estimates of the Q-factor values are for theintervals between the source and receivers. In some implementations, aformat of this estimate can include a series of numerical values ofdepth or time and the respective Q values. In some implementations, theQ model can be built into a model including the voxel volume of FIG. 2 .Generally, seismic source far-field particle displacement values areunderstood to be proportional to the Vibroseis ground force (fromstandard Vibroseis theory), whereas the geophone receivers measureparticle velocity.

In an embodiment, zero-offset VSP downgoing-P energy direct arrivals areanalyzed for Q estimation. Here, Q analysis is performed using aspectral ratio. In practice, for reliable Q values, the process ensuresthat there is geophone coupling with the formation and that there isgood cement behind casing and consistent source signature. The spectralratio is computed to determine the attenuation or Q factor, for eachdepth trace with respect to the selected reference trace with-in thedesired frequency bandwidth, from the downgoing-P energy direct arrival.

Generally, the spectral ratio method for determining Q factors usesEquation (1) and Equation (2)

$\begin{matrix}{Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}} & (1)\end{matrix}$ $\begin{matrix}{m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}} & (2)\end{matrix}$

where A₁ and A₂ are the spectral amplitudes for direct arrivals attravel times t₁ and t₂ recorded by receivers at depths d₁ and d₂, f isfrequency, ln is the natural logarithm, and m is the log of the ratio ofthe spectral amplitudes. m is obtained by measuring the slope of theline fitted (such as using a least square method) to the log of thespectral amplitude ratios plotted as a function of frequency. These givean interval Q factor between d₁ and d₂. The minimum and maximumfrequencies for this spectral ratio method are determined by inspection.In some implementations, the frequencies can be approximately 10 to 50Hz. The frequencies can vary depending on the source sweepspecifications and noise content. To fit straight lines to thesespectral ratios, noisy band edges are generally avoided. The recordedtrace at d₁ can be called a reference trace. In the present disclosure,the reference trace is the source Klauder wavelet trace, and A₁ is theKlauder amplitude spectra at time t₁=0 seconds and d₁=0 meters.

For determining the Q factor for the shallow region from the source atthe marine surface to the first downhole receiver, A₁ is the Klauderwavelet amplitude spectra at time t₁=0 seconds and depth d₁=0 meters,and A₂ is the amplitude spectra of the direct arrival event at time t₂of the first useable downhole receiver from the ground level surface atdepth d₂=0 meters.

FIG. 7 shows an example of a Q model 270 that is generated as a resultof the processes described in reference to FIGS. 5-6 . Wellbore 276 ispresent in the seismic environment in a marine region 280, similar towellbore 117 of FIG. 1 . Dual-sensor 278, including a hydrophone andgeophone as previously described, and receiver sensors 272 a-d (similarto sensors 116) are placed at or in the wellbore 276. Sensor 272 a is atdepth d₁, 272 b is at depth d₂, and forth until the nth sensor 272 d atdepth d_(n). Any number of sensors 272 can be included, but four sensorsare shown for illustrative purposes. Distances d₁-d_(n) can be deeperthan 1 kilometer. For each interval between receivers 272 a-d, aninterval Q value Q and associated time t are generated for the wellboreto generate a wellbore profile. This can be performed using Equations 1and 2 as previously discussed. The number n of Q_(n) values is based onthe number n of sensors 272. As previously stated, the Q values {Q₁, t₁,Q₂, t₂, . . . Q_(n), t_(n)} can form a model that is a number sequenceor a more complex 3-dimensional representation for combining with thevoxel model of FIG. 2 . The use of the dual sensor provides for moreaccurate Q values near the surface, such as for Q₁, t₁, or any of theother Q values shown for FIG. 7 .

FIG. 9 is a block diagram of an example computing system 400 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and proceduresdescribed in the present disclosure, according to some implementationsof the present disclosure. The illustrated computer 402 is intended toencompass any computing device such as a server, a desktop computer, alaptop/notebook computer, a wireless data port, a smart phone, apersonal data assistant (PDA), a tablet computing device, or one or moreprocessors within these devices, including physical instances, virtualinstances, or both. The computer 402 can include input devices such askeypads, keyboards, and touch screens that can accept user information.Also, the computer 402 can include output devices that can conveyinformation associated with the operation of the computer 402. Theinformation can include digital data, visual data, audio information, ora combination of information. The information can be presented in agraphical user interface (UI) (or GUI).

The computer 402 can serve in a role as a client, a network component, aserver, a database, a persistency, or components of a computer systemfor performing the subject matter described in the present disclosure.The illustrated computer 402 is communicably coupled with a network 424.In some implementations, one or more components of the computer 402 canbe configured to operate within different environments, includingcloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a high level, the computer 402 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 402 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 402 can receive requests over network 424 from a clientapplication (for example, executing on another computer 402). Thecomputer 402 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 402 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 402 can communicate using asystem bus 404. In some implementations, any or all of the components ofthe computer 402, including hardware or software components, caninterface with each other or the interface 406 (or a combination ofboth), over the system bus 404. Interfaces can use an applicationprogramming interface (API) 414, a service layer 416, or a combinationof the API 414 and service layer 416. The API 414 can includespecifications for routines, data structures, and object classes. TheAPI 414 can be either computer-language independent or dependent. TheAPI 414 can refer to a complete interface, a single function, or a setof APIs.

The service layer 416 can provide software services to the computer 402and other components (whether illustrated or not) that are communicablycoupled to the computer 402. The functionality of the computer 402 canbe accessible for all service consumers using this service layer.Software services, such as those provided by the service layer 416, canprovide reusable, defined functionalities through a defined interface.For example, the interface can be software written in JAVA, C++, or alanguage providing data in extensible markup language (XML) format.While illustrated as an integrated component of the computer 402, inalternative implementations, the API 414 or the service layer 416 can bestand-alone components in relation to other components of the computer402 and other components communicably coupled to the computer 402.Moreover, any or all parts of the API 414 or the service layer 416 canbe implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of the present disclosure.

The computer 402 includes an interface 406. Although illustrated as asingle interface 406 in FIG. 9 , two or more interfaces 406 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 402 and the described functionality. The interface 406 canbe used by the computer 402 for communicating with other systems thatare connected to the network 424 (whether illustrated or not) in adistributed environment. Generally, the interface 406 can include, or beimplemented using, logic encoded in software or hardware (or acombination of software and hardware) operable to communicate with thenetwork 424. More specifically, the interface 406 can include softwaresupporting one or more communication protocols associated withcommunications. As such, the network 424 or the hardware of theinterface can be operable to communicate physical signals within andoutside of the illustrated computer 402.

The computer 402 includes a processor 408. Although illustrated as asingle processor 408 in FIG. 9 , two or more processors 408 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 402 and the described functionality. Generally, theprocessor 408 can execute instructions and can manipulate data toperform the operations of the computer 402, including operations usingalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure.

The computer 402 also includes a database 420 that can hold data (forexample, seismic data 422) for the computer 402 and other componentsconnected to the network 424 (whether illustrated or not). For example,database 420 can be an in-memory, conventional, or a database storingdata consistent with the present disclosure. In some implementations,database 420 can be a combination of two or more different databasetypes (for example, hybrid in-memory and conventional databases)according to particular needs, desires, or particular implementations ofthe computer 402 and the described functionality. Although illustratedas a single database 420 in FIG. 9 , two or more databases (of the same,different, or combination of types) can be used according to particularneeds, desires, or particular implementations of the computer 402 andthe described functionality. While database 420 is illustrated as aninternal component of the computer 402, in alternative implementations,database 420 can be external to the computer 402.

The computer 402 also includes a memory 410 that can hold data for thecomputer 402 or a combination of components connected to the network 424(whether illustrated or not). Memory 410 can store any data consistentwith the present disclosure. In some implementations, memory 410 can bea combination of two or more different types of memory (for example, acombination of semiconductor and magnetic storage) according toparticular needs, desires, or particular implementations of the computer402 and the described functionality. Although illustrated as a singlememory 410 in FIG. 9 , two or more memories 410 (of the same, different,or combination of types) can be used according to particular needs,desires, or particular implementations of the computer 402 and thedescribed functionality. While memory 410 is illustrated as an internalcomponent of the computer 402, in alternative implementations, memory410 can be external to the computer 402.

The application 412 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 402 and the described functionality. Forexample, application 412 can serve as one or more components, modules,or applications. Further, although illustrated as a single application412, the application 412 can be implemented as multiple applications 412on the computer 402. In addition, although illustrated as internal tothe computer 402, in alternative implementations, the application 412can be external to the computer 402.

The computer 402 can also include a power supply 418. The power supply418 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 418 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 418 caninclude a power plug to allow the computer 402 to be plugged into a wallsocket or a power source to, for example, power the computer 402 orrecharge a rechargeable battery.

There can be any number of computers 402 associated with, or externalto, a computer system containing computer 402, with each computer 402communicating over network 424. Further, the terms “client,” “user,” andother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 402 and one user can use multiple computers 402.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Software implementations of the described subjectmatter can be implemented as one or more computer programs. Eachcomputer program can include one or more modules of computer programinstructions encoded on a tangible, non-transitory, computer-readablecomputer-storage medium for execution by, or to control the operationof, data processing apparatus. Alternatively, or additionally, theprogram instructions can be encoded in/on an artificially generatedpropagated signal. The example, the signal can be a machine-generatedelectrical, optical, or electromagnetic signal that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer-storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofcomputer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electroniccomputer device” (or equivalent as understood by one of ordinary skillin the art) refer to data processing hardware. For example, a dataprocessing apparatus can encompass all kinds of apparatus, devices, andmachines for processing data, including by way of example, aprogrammable processor, a computer, or multiple processors or computers.The apparatus can also include special purpose logic circuitryincluding, for example, a central processing unit (CPU), a fieldprogrammable gate array (FPGA), or an application specific integratedcircuit (ASIC). In some implementations, the data processing apparatusor special purpose logic circuitry (or a combination of the dataprocessing apparatus or special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The apparatus can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present disclosure contemplates the use of data processingapparatuses with or without conventional operating systems, for example,LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code, can be written in any form of programming language.Programming languages can include, for example, compiled languages,interpreted languages, declarative languages, or procedural languages.Programs can be deployed in any form, including as stand-alone programs,modules, components, subroutines, or units for use in a computingenvironment. A computer program can, but need not, correspond to a filein a file system. A program can be stored in a portion of a file thatholds other programs or data, for example, one or more scripts stored ina markup language document, in a single file dedicated to the program inquestion, or in multiple coordinated files storing one or more modules,sub programs, or portions of code. A computer program can be deployedfor execution on one computer or on multiple computers that are located,for example, at one site or distributed across multiple sites that areinterconnected by a communication network. While portions of theprograms illustrated in the various figures may be shown as individualmodules that implement the various features and functionality throughvarious objects, methods, or processes, the programs can instead includea number of sub-modules, third-party services, components, andlibraries. Conversely, the features and functionality of variouscomponents can be combined into single components as appropriate.Thresholds used to make computational determinations can be statically,dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specificationcan be performed by one or more programmable computers executing one ormore computer programs to perform functions by operating on input dataand generating output. The methods, processes, or logic flows can alsobe performed by, and apparatus can also be implemented as, specialpurpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be basedon one or more of general and special purpose microprocessors and otherkinds of CPUs. The elements of a computer are a CPU for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a CPU can receive instructions anddata from (and write data to) a memory. A computer can also include, orbe operatively coupled to, one or more mass storage devices for storingdata. In some implementations, a computer can receive data from, andtransfer data to, the mass storage devices including, for example,magnetic, magneto optical disks, or optical disks. Moreover, a computercan be embedded in another device, for example, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a global positioning system (GPS) receiver, or a portablestorage device such as a universal serial bus (USB) flash drive.

Computer readable media (transitory or non-transitory, as appropriate)suitable for storing computer program instructions and data can includeall forms of permanent/non-permanent and volatile/non-volatile memory,media, and memory devices. Computer readable media can include, forexample, semiconductor memory devices such as random access memory(RAM), read only memory (ROM), phase change memory (PRAM), static randomaccess memory (SRAM), dynamic random access memory (DRAM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices.Computer readable media can also include, for example, magnetic devicessuch as tape, cartridges, cassettes, and internal/removable disks.Computer readable media can also include magneto optical disks andoptical memory devices and technologies including, for example, digitalvideo disc (DVD), CD ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY.The memory can store various objects or data, including caches, classes,frameworks, applications, modules, backup data, jobs, web pages, webpage templates, data structures, database tables, repositories, anddynamic information. Types of objects and data stored in memory caninclude parameters, variables, algorithms, instructions, rules,constraints, and references. Additionally, the memory can include logs,policies, security or access data, and reporting files. The processorand the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry.

Implementations of the subject matter described in the presentdisclosure can be implemented on a computer having a display device forproviding interaction with a user, including displaying information to(and receiving input from) the user. Types of display devices caninclude, for example, a cathode ray tube (CRT), a liquid crystal display(LCD), a light-emitting diode (LED), and a plasma monitor. Displaydevices can include a keyboard and pointing devices including, forexample, a mouse, a trackball, or a trackpad. User input can also beprovided to the computer through the use of a touchscreen, such as atablet computer surface with pressure sensitivity or a multi-touchscreen using capacitive or electric sensing. Other kinds of devices canbe used to provide for interaction with a user, including to receiveuser feedback including, for example, sensory feedback including visualfeedback, auditory feedback, or tactile feedback. Input from the usercan be received in the form of acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending documents to,and receiving documents from, a device that is used by the user. Forexample, the computer can send web pages to a web browser on a user'sclient device in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, including,but not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include aplurality of user interface (UI) elements, some or all associated with aweb browser, such as interactive fields, pull-down lists, and buttons.These and other UI elements can be related to or represent the functionsof the web browser.

Implementations of the subject matter described in this specificationcan be implemented in a computing system that includes a back endcomponent, for example, as a data server, or that includes a middlewarecomponent, for example, an application server. Moreover, the computingsystem can include a front-end component, for example, a client computerhaving one or both of a graphical user interface or a Web browserthrough which a user can interact with the computer. The components ofthe system can be interconnected by any form or medium of wireline orwireless digital data communication (or a combination of datacommunication) in a communication network. Examples of communicationnetworks include a local area network (LAN), a radio access network(RAN), a metropolitan area network (MAN), a wide area network (WAN),Worldwide Interoperability for Microwave Access (WIMAX), a wirelesslocal area network (WLAN) (for example, using 402.11 a/b/g/n or 402.20or a combination of protocols), all or a portion of the Internet, or anyother communication system or systems at one or more locations (or acombination of communication networks). The network can communicatewith, for example, Internet Protocol (IP) packets, frame relay frames,asynchronous transfer mode (ATM) cells, voice, video, data, or acombination of communication types between network addresses.

The computing system can include clients and servers. A client andserver can generally be remote from each other and can typicallyinteract through a communication network. The relationship of client andserver can arise by virtue of computer programs running on therespective computers and having a client-server relationship.

Cluster file systems can be any file system type accessible frommultiple servers for read and update. Locking or consistency trackingmay not be necessary since the locking of exchange file system can bedone at application layer. Furthermore, Unicode data files can bedifferent from non-Unicode data files.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular implementations. Certain features that are described in thisspecification in the context of separate implementations can also beimplemented, in combination, in a single implementation. Conversely,various features that are described in the context of a singleimplementation can also be implemented in multiple implementations,separately, or in any suitable sub-combination. Moreover, althoughpreviously described features may be described as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations may be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

While this specification contains many details, these should not beconstrued as limitations on the scope of what may be claimed, but ratheras descriptions of features specific to particular examples. Certainfeatures that are described in this specification in the context ofseparate implementations can also be combined. Conversely, variousfeatures that are described in the context of a single implementationcan also be implemented in multiple embodiments separately or in anysuitable sub-combination.

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the data processing system described herein.Accordingly, other embodiments are within the scope of the followingclaims.

What is claimed is:
 1. A method for determining a seismic attenuationquality factor Q for seismic signals at intervals of subsurfaceformations between a seismic source at a marine level surface and one ormore receivers of a well, the method comprising: obtaining sensor datafrom a dual sensor comprising a hydrophone and a geophone, the sensordata including a hydrophone component and a geophone component;generating a reference trace from the hydrophone component and thegeophone component of the sensor data; receiving vertical seismicprofile traces; perform a first break picking of the vertical seismicprofile traces; generating, based on the first break picking, verticalseismic profile data representing particle motion measured by a firstreceiver of the one or more receivers of the well; injecting thereference trace into the vertical seismic profile data; determining aratio of spectral amplitudes of a direct arrival event of the verticalseismic profile data and the reference trace; and generating, from theratio, a quality factor Q representing a time and depth compensatedattenuation value of seismic signals between the seismic source at themarine level surface and the first receiver.
 2. The method of claim 1,wherein generating a reference trace from the hydrophone component andthe geophone component of the sensor data comprises: applying a matchingfilter to transform the hydrophone component of the sensor data to matchthe geophone component; and summing the transformed hydrophone componentand the geophone component.
 3. The method of claim 1, furthercomprising: comparing each trace of the vertical seismic profile tracesto a quality threshold; and removing, from the vertical seismic profiletraces, each trace that does not satisfy the quality threshold.
 4. Themethod of claim 1, further comprising: determining a geometry of thewell and a location of the dual sensor; and performing a correction onthe vertical seismic profile traces to account for a geometricalspreading of propagating seismic signals used to generate the verticalseismic profile data.
 5. The method of claim 1, wherein the dual sensoris deployed at or near a well location at the marine level surface ofthe subsurface formation.
 6. The method of claim 1, wherein the ratio isdefined by ${Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}},$${m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}},$ where A₁ and A₂are the spectral amplitudes for direct arrivals at travel times, t₁ andt₂ recorded by receivers at depths d₁ and d₂, f is frequency, ln is anatural logarithm, and m is the natural logarithm of the ratio of thespectral amplitudes.
 7. The method of claim 6, wherein A₁ is a Klauderwavelet amplitude spectra at time t₁=0 seconds and depth d₁=0 meters,and A₂ is the amplitude spectra of the direct arrival event at time t₂of the first receiver from the marine level surface at depth d₂=0meters.
 8. A system for determining a seismic attenuation quality factorQ for seismic signals at intervals of subsurface formations between aseismic source at a marine level surface and one or more receivers of awell, the system comprising: at least one processor; and a memorystoring instructions that, when executed by the at least one processor,cause the at least one processor to perform operations comprising:obtaining sensor data from a dual sensor comprising a hydrophone and ageophone, the sensor data including a hydrophone component and ageophone component; generating a reference trace from the hydrophonecomponent and the geophone component of the sensor data; receivingvertical seismic profile traces; perform a first break picking of thevertical seismic profile traces; generating, based on the first breakpicking, vertical seismic profile data representing particle motionmeasured by a first receiver of the one or more receivers of the well;injecting the reference trace into the vertical seismic profile data;determining a ratio of spectral amplitudes of a direct arrival event ofthe vertical seismic profile data and the reference trace; andgenerating, from the ratio, a quality factor Q representing a time anddepth compensated attenuation value of seismic signals between theseismic source at the marine level surface and the first receiver. 9.The system of claim 8, wherein generating a reference trace from thehydrophone component and the geophone component of the sensor datacomprises: applying a matching filter to transform the hydrophonecomponent of the sensor data to match the geophone component; andsumming the transformed hydrophone component and the geophone component.10. The system of claim 8, the operations further comprising: comparingeach trace of the vertical seismic profile traces to a qualitythreshold; and removing, from the vertical seismic profile traces, eachtrace that does not satisfy the quality threshold.
 11. The system ofclaim 8, the operations further comprising: determining a geometry ofthe well and a location of the dual sensor; and performing a correctionon the vertical seismic profile traces to account for a geometricalspreading of propagating seismic signals used to generate the verticalseismic profile data.
 12. The system of claim 8, wherein the dual sensoris deployed at or near a well location at the marine level surface ofthe subsurface formation.
 13. The system of claim 8, wherein the ratiois defined by ${Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}},$${m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}},$ where A₁ and A₂are the spectral amplitudes for direct arrivals at travel times, t₁ andt₂ recorded by receivers at depths d₁ and d₂, f is frequency, ln is anatural logarithm, and m is the natural logarithm of the ratio of thespectral amplitudes.
 14. The system of claim 13, wherein A₁ is a Klauderwavelet amplitude spectra at time t₁=0 seconds and depth d₁=0 meters,and A₂ is the amplitude spectra of the direct arrival event at time t₂of the first receiver from the marine level surface at depth d₂=0meters.
 15. One or more non-transitory computer readable media storinginstructions for determining a seismic attenuation quality factor Q forseismic signals at intervals of subsurface formations between a seismicsource at a marine level surface and one or more receivers of a well,the instructions being configured to cause at least one processor, whenexecuted by the at least one processor, to perform operationscomprising: obtaining sensor data from a dual sensor comprising ahydrophone and a geophone, the sensor data including a hydrophonecomponent and a geophone component; generating a reference trace fromthe hydrophone component and the geophone component of the sensor data;receiving vertical seismic profile traces; perform a first break pickingof the vertical seismic profile traces; generating, based on the firstbreak picking, vertical seismic profile data representing particlemotion measured by a first receiver of the one or more receivers of thewell; injecting the reference trace into the vertical seismic profiledata; determining a ratio of spectral amplitudes of a direct arrivalevent of the vertical seismic profile data and the reference trace; andgenerating, from the ratio, a quality factor Q representing a time anddepth compensated attenuation value of seismic signals between theseismic source at the marine level surface and the first receiver. 16.The one or more non-transitory computer readable media of claim 15,wherein generating a reference trace from the hydrophone component andthe geophone component of the sensor data comprises: applying a matchingfilter to transform the hydrophone component of the sensor data to matchthe geophone component; and summing the transformed hydrophone componentand the geophone component.
 17. The one or more non-transitory computerreadable media of claim 15, the operations further comprising: comparingeach trace of the vertical seismic profile traces to a qualitythreshold; and removing, from the vertical seismic profile traces, eachtrace that does not satisfy the quality threshold.
 18. The one or morenon-transitory computer readable media of claim 15, the operationsfurther comprising: determining a geometry of the well and a location ofthe dual sensor; and performing a correction on the vertical seismicprofile traces to account for a geometrical spreading of propagatingseismic signals used to generate the vertical seismic profile data. 19.The one or more non-transitory computer readable media of claim 15,wherein the dual sensor is deployed at or near a well location at themarine level surface of the subsurface formation.
 20. The one or morenon-transitory computer readable media of claim 15, wherein the ratio isdefined by ${Q = {- {\pi\left( \frac{t_{2} - t_{1}}{m} \right)}}},$${m = {\ln\left( \frac{A_{2}(f)}{A_{1}(f)} \right)}},$ where A₁ and A₂are the spectral amplitudes for direct arrivals at travel times, t₁ andt₂ recorded by receivers at depths d₁ and d₂, f is frequency, ln is anatural logarithm, and m is the natural logarithm of the ratio of thespectral amplitudes.